Using a neural network with flow cytometry histograms to recognize cell surface protein binding patterns

Proc AMIA Symp. 2002:380-4.


Flow cytometric systems are being used increasingly in all branches of biological science including medicine. To develop analytic tools for identifying unknown molecules such as the antibodies that recognize different structure in the identical antigens, we explored use of a neural network in flow cytometry data comparison. Peak locations were extracted from flow cytometry histograms and we used the Marquardt backpropagation neural networks to recognize identical or similar binding patterns between antibodies and antigens based on the peak locations. The neural network showed 93.8% to 99.6% correct classification rates for identical or similar molecules. This suggests that the neural network technique can be useful in flow cytometry histogram data analysis.

Publication types

  • Evaluation Study

MeSH terms

  • Antibodies, Monoclonal / metabolism*
  • Antigens, Surface / metabolism*
  • Flow Cytometry*
  • Lymphocytes / metabolism
  • Membrane Proteins / metabolism
  • Neural Networks, Computer*
  • Protein Binding


  • Antibodies, Monoclonal
  • Antigens, Surface
  • Membrane Proteins